Patient outcomes associated with subcutaneous C1INH prophylaxis for hereditary angioedema: a retrospective analysis
Bibliographic record
Abstract
BACKGROUND: Real-world data on subcutaneous C1INH (C1INH[SC]) usage and patient-level impacts on hereditary angioedema (HAE)-related outcomes and quality of life (QoL) are both lacking and challenging to generate using conventional study methodologies. Using a hybrid study design involving patient interviews supplemented by retrospective medical chart data review, we conducted a real-world assessment of the impact of C1INH(SC) prophylaxis on HAE attack patterns, QoL, and on-demand medication use. METHODS: The study was conducted at seven US sites and included 36 adults with HAE who had been treated with C1INH(SC) long-term prophylaxis following ≥ 12 months of on-demand management only. Patients underwent 30-min interviews, facilitated and analyzed by a trained qualitative research specialist. Medical records were reviewed for 12 months before (pre-index) and after (post-index) initiation of C1INH(SC). Using interview data with descriptive terms converted to numerical values, we compared pre- versus post-index attack frequency, severity, and rescue medication usage. RESULTS: Mean (SD) annualized attack frequency per patient decreased 82.0%, from 38.8 (38.8) attacks/year pre-index to 7.0 (15.3) attacks/year (P < 0.001); the median number of attacks decreased by 97.0% (30 pre-index to 1 post-index). For 20 patients, the annualized attack rate after starting C1INH(SC) prophylaxis was ≤ 1 attack/year; 12 of these patients reported 0 attacks. Mean (SD) attack severity (scale: 0 = none/mild to 4 = very severe) decreased from 2.3 (0.7) pre-index to 0.9 (0.9) post-index (P < 0.001). Mean/median rescue medication use decreased by 77.2%/96.3%. Improved QoL was narratively described for many domains. CONCLUSIONS: These real-world findings indicate that long-term prophylaxis with C1INH(SC) markedly improves important factors that contribute to the goal of achieving total disease control and normalization of patients' lives, including fewer and less severe attacks, less rescue medication usage, and improved QoL.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".